Cloud-AI-Architect Exam Information and Outline
Cloud AI Architect
Syllabus Update Checked on: November 10, 2026
Cloud-AI-Architect Exam Syllabus & Study Guide
Before you start practicing with our exam simulator, it is essential to understand the
official Cloud-AI-Architect exam objectives. This course outline serves as your roadmap.
The information below reflects the 2026 syllabus defined by
Arcitura.
Below are complete topics detail with latest syllabus and course outline, that will help you good knowledge about exam objectives and topics that you have to prepare. These contents are covered in questions and answers pool of exam.
- Exam Format
- Online-proctored multiple-choice certification exam (Pearson VUE) covering the modules below
- Practice exam questions provided with the certification bundle
- Module 1: Fundamental Cloud Computing
- Cloud terminology, concepts and virtualization basics
- Cloud characteristics: on-demand usage, elasticity, resiliency, measured usage
- Benefits, challenges, risks and cloud roles
- Delivery models: SaaS, PaaS, IaaS
- Deployment models: public, private, hybrid, community
- Cost metrics and SLA quality-of-service formulas
- Module 2: Cloud Technology Concepts
- Cloud architecture mechanisms: virtual servers, failover, scaling, load balancing
- Containerization vs. virtualization
- Cloud balancing and cloud bursting
- Cloud security threats and countermeasures
- Cloud storage; NoSQL vs. relational storage
- Service implementation (Web and REST services) and testing considerations
- Industry standards organizations
- Module 3: Fundamental Predictive AI
- Business and technology drivers, benefits and risks
- Predictive AI types and business problem categories
- Learning approaches: supervised, unsupervised, semi-supervised, reinforcement
- Model training loop; AI models and neural networks
- Functional designs: computer vision, NLP, speech recognition, robotics
- Module 4: Fundamental Generative AI
- Business drivers, benefits, risks and problem categories
- Generative models, algorithms and neural networks
- GANs, VAEs and Transformers
- Steps to building AI systems and best practices
- Module 5: Cloud AI Technology & Automation
- Cloud infrastructure for AI: GPUs and TPUs
- Cloud AI services: AWS SageMaker, Azure Machine Learning, Google AI Platform
- AI data storage: object storage, data warehouses, data lakes
- Training, deploying and monitoring models in the cloud
- Compliance, governance, security and privacy
- Module 6: Cloud AI Architecture & Design
- High availability and real-time data architectures
- Microservices, service meshes and zero-trust security
- Lifecycle management, observability, disaster recovery and interoperability
- Module 7: Cloud AI Architecture & Design Lab
- Case-study lab exercises